Consistency-Guided Differential Decoding for Enhancing Semi-Supervised Medical Image Segmentation.

Area of Science:

• Medical Image Analysis
• Machine Learning
• Computer Vision

Background:

• Limited labeled data is a major challenge in volumetric medical image segmentation.
• Existing semi-supervised learning (SSL) methods often rely on pseudo-labeling or consistency regularization.
• Differential decoder features can naturally emerge when models aim for prediction consistency.

Purpose of the Study:

• To propose a novel SSL method, LeFeD, that capitalizes on differential decoder features.
• To enhance semi-supervised medical image segmentation performance by learning from feature discrepancies.
• To establish a new state-of-the-art in the field.

Main Methods:

• Analyzing the value of discrepancies in learning for consistency under SSL settings.
• Proposing LeFeD, which learns feature-level discrepancies from two decoders.
• Feeding these discrepancies as feedback signals to the encoder to iteratively refine learning.

Main Results:

• LeFeD outperforms eight state-of-the-art methods on three public datasets.
• The method achieves superior results without complex additions like uncertainty estimation.
• LeFeD sets a new state-of-the-art benchmark for semi-supervised medical image segmentation.

Conclusions:

• LeFeD effectively utilizes differential decoder features for improved semi-supervised segmentation.
• The proposed method offers a simpler yet powerful approach compared to existing SSL techniques.
• LeFeD demonstrates significant advancements in medical image segmentation with limited labeled data.